Question

[Modeling] A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

Answer

Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

Answer

Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.

Answer

Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

[Modeling] A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point…

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